Scaling up: linking field data and remote sensing with a hierarchical model

Abstract

Ecologists often seek to understand patterns and processes across multiple spatial and temporal scales ranging from centimeters to hundreds of meters and from seconds to years. Hierarchical statistical models offer a framework for sampling design and analysis that can be used to incorporate the information collected at finer scales while allowing comparison at coarser scales. In this study we use a Hierarchical Bayesian model to assess the relationship between measurements collected on the ground at centimeter scales nested within 2 × 3 m quadrats, which are in turn nested within much larger (0.1–12 ha) plots. We compare these measurements with the Normalized Difference Vegetation Index (NDVI) derived from radiometrically and geometrically corrected 30-m resolution LANDSAT ETM+ data to assess the NDVI–Biomass relationship in the Cape Floristic Region of South Africa. Our novel modeling approach allows the data observed at submeter scales to be incorporated directly into the model and thus all the data (and variability) collected at finer scales are represented in the estimates of biomass at the LANDSAT scale. The model reveals that there is a strong correlation between NDVI and biomass, which supports the use of NDVI in spatiotemporal analysis of vegetation dynamics in Mediterranean shrubland ecosystems. The methods developed here can be easily generalized to other ecosystems and ecophysiological parameters.

Keywords:

• biomass
• remote sensing
• ground truthing
• scale
• Hierarchical Bayes

Acknowledgments

We thank the Western Cape Nature Conservation Board for permission to work in the protected areas of the Western Cape. This research was supported by NSF grants OISE-0623341 and DEB0516320 to JAS, by NASA headquarters under the NASA Earth and Space Science Fellowship Program grant NNX09AN82H to AMW, a NASA CT Space Grant to AMW, and a grant from the University of Connecticut Center for Environmental Science and Engineering to AMW. We also thank the anonymous reviewers for suggestions that helped improve the manuscript.